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Financial distress is a socially and economically important problem that affects companies the world over. Having the power to better understand – and hence aid businesses from failing, has the potential to save not only the company, but also potentially prevent economies from sustained downturn. Although Islamic banks constitute a fraction of total banking assets, their importance have been substantially increasing, as their asset growth rate has surpassed that of conventional banks in recent years. The paper aims to discuss these issues.

This paper uses a data set comprising 101 international publicly listed Islamic banks to work on advancing financial distress prediction (FDP) by utilising cutting-edge stochastic models, namely decision trees, stochastic gradient boosting and random forests. The most important variables pertaining to forecasting corporate failure are determined from an initial set of 18 variables.

The results indicate that the “Working Capital/Total Assets” ratio is the most crucial variable relating to forecasting financial distress using both the traditional “Altman Z-Score” and the “Altman Z-Score for Service Firms” methods. However, using the “Standardised Profits” method, the “Return on Revenue” ratio was found to be the most important variable. This provides empirical evidence to support the recommendations made by Basel Accords for assessing a bank’s capital risks, specifically in relation to the application to Islamic banking.

These findings provide a valuable addition to the limited literature surrounding Islamic banking in general, and FDP pertaining to Islamic banking in particular, by showcasing the most pertinent variables in forecasting financial distress so that appropriate proactive actions can be taken.

Rising trends in alcohol consumption and early drinking initiation pose serious health risks especially for adolescents. Learner’s prior knowledge about alcohol gained from the social surroundings and the media are important sources that can impact the learning outcomes in health education. The purpose of this paper is to map adolescents’ perceptions of alcohol in Punjab, India and how these perceptions are related to their attitudes towards their social surroundings and the media.

The questionnaire was created after informal discussions with local people who consume alcohol and discussions with alcohol-related experts. Students from five schools (n=379, average age=13.6 years) in the urban region of Punjab, India, filled in a questionnaire. Quantitative tests were performed on the questionnaire data. Summative content analysis was performed for the textbook content about alcohol from classes 1 to 10.

Data suggest that students gain knowledge about alcohol from multiple sources, including society, the media and education. While society and the media can give misinformation, education did not provide them with factual scientific information about alcohol. Students from financially marginalized social surroundings experience the presence and use of alcohol more frequently; they trust the media and celebrities somewhat unquestioningly and, hence, are more at-risk.

All participants in informal discussions as well as all participating schools in the study were from urban regions. Data about individual’s socio-economic conditions was not collected.

This research investigates perceptions of alcohol that are derived from adolescents’ social surroundings, perceptions of the media and perceptions gained through educational guidance in a developing country. Such multi-dimensional investigations have not been conducted earlier.

The purpose of this study is to review the literature on money laundering and its related areas. The main objective is to identify any gaps in the literature and direct attention towards addressing them.

A systematic review of the money laundering literature was conducted with an emphasis on the Pro-Quest, Scopus and Science-Direct databases. Broad research themes were identified after investigating the literature. The theme about the detection of money laundering was then further investigated. The major approaches of such detection are identified, as well as research gaps that could be addressed in future studies.

The literature on money laundering can be classified into the following six broad areas: anti-money laundering framework and its effectiveness, the effect of money laundering on other fields and the economy, the role of actors and their relative importance, the magnitude of money laundering, new opportunities available for money laundering and detection of money laundering. Most studies about the detection of money laundering have focused on the use of innovative technologies, banking transactions or real estate- and trade-based money laundering. However, the literature on the detection of shell companies being explicitly used to launder funds is relatively scarce.

This paper provides insights into an area related to money laundering where research is relatively scant. Shell companies incorporated in the UK alone were identified to be associated with laundering £80bn of stolen money between 2010 and 2014. The use of these entities to launder billions of dollars as witnessed through the laundromat schemes and several data leaks clearly indicate the need to focus on illicit financial flows through such entities.

Recent research has confirmed an underlying economic logic that connects each of the three vertices of the “fraud triangle” – a fundamental criminological model of factors driving occupational fraud. It is postulated that in the presence of economic motivation and opportunity (the first two vertices of the fraud triangle), the likelihood of an occupational fraud happening in an organization increases substantially if the overall organization culture is perceived as being slack toward fraud as it helps potential fraudsters in rationalizing their actions (rationalization being the third vertex of the fraud triangle). This paper aims to offer a viable approach for collecting and processing of data to identify and operationalize the key factors underlying employee perception of organization culture toward occupational frauds.

This paper reports and analyses the results of a pilot study conducted using a convenience sampling approach to identify and operationalize the key factors underlying employee perception of organization culture with respect to occupational frauds. Given a very small sample size, a numerical testing technique based on the binomial distribution has been applied to test for significance of the proportion of respondents who agree that a lenient organizational culture toward fraud can create a rationalization for fraud.

The null hypothesis assumed no difference in the population proportions between those who agree and those who disagree with the view that a lenient organizational culture toward fraud can create a rationalization for fraud. Based on the results of the numerical test, the null hypothesis is rejected in favor of the alternative that the population proportion of those who agree with the stated view in fact exceeded the proportion of those who disagreed.

The obvious limitation is the very small size of the sample obtained because of an extremely low rate of response to the survey questionnaires. However, while of course a much bigger data set needs to be collected to develop a generalizable prediction model, the small sample was enough for the purpose of a pilot study.

This paper makes two distinct practical contributions. First, it posits a viable empirical research plan for identifying, collecting and processing the right data to identify and operationalize the key underlying factors that capture an employee’s perception of organizational culture toward fraud as a basis for rationalizing an act of fraud. Second, it demonstrates via a small-scale pilot study that a more broad-based survey can indeed prove to be extremely useful in collating the sort of data that is needed to develop a computational model for predicting the likelihood of occupational fraud in any organization.

This paper provides a viable framework which empirical researchers can follow to test some of the latest advances in the “fraud triangle” theory. It outlines a systematic and focused data collection method via a well-designed questionnaire that is effectively applicable to future surveys that are scaled up to collect data at a nationwide level.

A dynamical two‐dimensional problem of a homogeneous transversely isotropic fibre‐reinforced generalized thermoelastic solid with an overlying acoustic fluid layer has been considered to investigate disturbance due to mechanical load. Laplace and Fourier transform techniques are applied to solve the problem. Uniformly distributed and linearly distributed forces are applied to illustrate the utility of the approach. A numerical inversion technique has been applied to obtain the solution in the physical domain. Numerical results are obtained and presented graphically to show the effect of anisotropy along with the comparison of homogeneous transversely isotropic fibre‐reinforced generalized thermoelastic solid and isotropic elastic solid.

In today's internationalized world, value creation consists of knowledge and work integration involving workers from around the world. Members of these globally distributed work teams (GDWT) encounter organizational behavior issues (identity, cultural differences, and leadership) and organization design issues (dependencies, information processing, media use, and teamwork structures). While most research on GDWT focuses on the first set of issues, this chapter is among the few to systematically explore the second set. We propose and elaborate on strategies for either reducing the intensity of collaboration, or enabling teams to collaborate intensely on a global scale. Implications for research and practice are explored.

Describes the ability of modern computer‐driven multivariate statistical analysis to deal with complex data and the development of statistical models for predicting financial distress. Applies multivariate techniques to 1986‐1991 financial ratio data for Australian failed (29) and nonfailed (42) companies; and explains the techniques used (principal components analysis, factor analysis, discriminant analysis and cluster analysis) and the different types of information they can provide to help identify the distress levels of companies. Predicts that multivariate methods will change the way researchers think about problems and design their research. An unusually clear exposition of the application of multivariate methods.

In recent years there has been a revolutionary approach in business, governments and industry due to advances in computer technology and efficient statistical methods. This paper examines and offers practical suggestions for those in human resource improvement in output quality and the workplace atmosphere in light of Deming’s principles of human resource management.

The purpose of this study is to describe the experiences with the development and use of an agile component‐based architecture for enabling the requirements for the transformation of financial services.

The methodology used is a case study. Findings –The findings of the case study indicate that while technically it is feasible to develop and implement such an architecture, a number of managerial and organizational issues need to be addressed before such architecture can become successful.

For the practitioners and managers in the financial services industry, this study provides a potential solution to its need for an agile technology platform that can keep aligned with its evolving business requirements.

The purpose of this paper is to perform a comparative study of prediction performances of an artificial neutral network (ANN) model against a linear prediction model like a linear discriminant analysis (LDA) with regards to forecasting corporate credit ratings from financial statement data.

The ANN model used in the study is a fully connected back‐propagation model with three layers of neurons. The paper uses a comparative approach whereby two prediction models – one based on ANN and the other based on LDA are developed using identically partitioned data set.

The study found that the ANN model comprehensively outperformed the LDA model in both training and test partitions of the data set. While the LDA model may have been hindered by omitted variables; this actually lends further credence to the ANN model showing that the latter is more robust in dealing with missing data.

A possible drawback in the model implementation probably lies in the selection of the various accounting ratios. Perhaps future replications of this study should look more carefully at choosing the ratios after duly addressing the problems of collinearity and duplications more rigorously.

The findings of this study imply that since ANN models can better deal with complex data sets and do not require restraining assumptions like linearity and normality, it may be overall a better approach in corporate credit rating forecasts that uses large financial data sets.

This study brings out the effectiveness of non‐linear pattern learning models as compared to linear ones in forecasts of financial solvency. This goes on to further highlight the practical importance of the new breed of computational tools available to techno‐savvy financial analysts and also to the providers of corporate credit.